Biological Conservation 181 (2015) 150–161
Contents lists available at ScienceDirect
Biological Conservation
journal homepage: www.elsevier.com/locate/biocon
Rapid multi-nation distribution assessment of a charismatic
conservation species using open access ensemble model GIS predictions:
Red panda (Ailurus fulgens) in the Hindu-Kush Himalaya region
Kamal Kandel a, Falk Huettmann b,⇑, Madan Krishna Suwal a, Ganga Ram Regmi a, Vincent Nijman c,
K.A.I. Nekaris c, Sonam Tashi Lama a, Arjun Thapa d, Hari Prasad Sharma e, Tulsi Ram Subedi f
a
Global Primate Network-Nepal, GPO Box 26288, Kathmandu, Nepal
-EWHALE LAB-, Inst. of Arctic Biology, Biology & Wildlife Dept., University of Alaska Fairbanks (UAF), USA
c
Anthropology Centre for Conservation, Environment and Development, Oxford Brookes University, Oxford, UK
d
Small Mammals Conservation and Research Foundation, Kathmandu, Nepal
e
Central Department of Zoology, Tribhuvan University, Kathmandu, Nepal
f
Himalayan Nature, Kathmandu, Nepal
b
a r t i c l e
i n f o
Article history:
Received 24 April 2014
Received in revised form 3 October 2014
Accepted 5 October 2014
Available online 1 December 2014
Keywords:
Red panda (Ailurus fulgens)
Hindu-Kush Himalaya (HKH)
Open access GBIF data
Machine learning
Ensemble model GIS prediction
a b s t r a c t
The red panda (Ailurus fulgens) is a globally threatened species living in the multi-national Hindu-Kush
Himalaya (HKH) region. It has a declining population trend due to anthropogenic pressures. Humandriven climate change is expected to have substantial impacts. However, quantitative and transparent
information on the ecological niche (potential as well as realized) of this species across the vast and
complex eight nations of the HKH region is lacking. Such baseline information is not only crucial for
identifying new populations but also for restoring locally-extinct populations, for understanding its
bio-geographical evolution, as well as for prioritizing regions and an efficient management.
First we compiled, and made publicly available through an institutional repository (dSPACE), the best
known ‘presence only’ red panda dataset with ISO compliant metadata. This was done through the
International Centre for Integrated Mountain Development (ICIMOD.org) data-platform to the Global
Biodiversity Information Facility (GBIF.org). We used data mining and machine learning algorithms such
as high-performance commercial Classification and Regression Trees, Random Forest, TreeNet, and
Multivariate Adaptive Regression Splines implementations. We averaged all these Geographic Information System (GIS) models for the first produced ensemble model for this species in the HKH region.
Our predictive model is the first of its kind and allows to assess the red panda distribution based on
empirical open access data, latest methods and the major signals and drivers of the ecological niche. It
allows to assess and fine-tune earlier habitat area estimates. Our models promote ‘best professional
practices’. It can readily be used by the red panda Recovery Team, the red panda Action Plan, etc. because
they are robust, transparent, publicly available, fit for use, and have a good accuracy, as judged by several
independent assessment metrics (Receiver Operating Characteristics (ROC-AUC) curves, expert opinion,
assessed by known absence regions, 95% confidence intervals and new field data).
Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction
⇑ Corresponding author at: 419 Irving 1, University of Alaska-Fairbanks, 99775
Fairbanks, AK, USA.
E-mail addresses: kandel.kamal82@gmail.com (K. Kandel), fhuettmann@alaska.
edu (F. Huettmann), madanksuwal@gmail.com (M.K. Suwal), regmigr1978@gmail.
com (G. Ram Regmi), vnijman@brookes.ac.uk (V. Nijman), anekaris@brookes.ac.
uk (K.A.I. Nekaris), sonam.tac@gmail.com (S.T. Lama), thapa.nature@gmail.com
(A. Thapa), himalayanhari@gmail.com (H.P. Sharma), tulsi.biologist@gmail.com
(T.R. Subedi).
http://dx.doi.org/10.1016/j.biocon.2014.10.007
0006-3207/Ó 2014 Elsevier Ltd. All rights reserved.
The distribution of a species is an inherent part of its ecology
(Krebs, 2009); such information is essential to know for a successful conservation-dependent species’ management (Braun, 2005).
The prediction of species’ distribution is central to diverse applications in ecology, evolution and conservation science (Guisan and
Zimmermann, 2000; Elith et al., 2006) and when the precautionary
principle is of increasing importance (Cushman and Huettmann,
2010; Drew et al., 2011; Huettmann, 2012). The potential
K. Kandel et al. / Biological Conservation 181 (2015) 150–161
distribution and quantified ecological niche describes where
conditions are suitable for an occurrence and for supporting the
survival of the species (Phillips et al., 2004; Cushman and
Huettmann, 2010; Drew et al., 2011). Distribution models can
describe such ecological niches and establish a quantitative relationship between the relative occurrence of the species and their
bio-physical and environmental conditions in the landscape
(Guisan and Zimmermann, 2000; Elith et al., 2006; Phillips et al.,
2004, 2006; Phillips and Dudik, 2008). This can even be the case
when the survey effort in a landscape is somewhat incomplete
and uneven (Kadmon et al., 2004) and when data are ‘messy’
(Craig and Huettmann, 2008). Such models provide essential information for monitoring and restoration of declining populations in
the natural habitat. They help to establish core conservation areas
and biological corridors for instance (Wilson et al., 2009). In addition to just quantifying the ecological niche of a species, species
distribution models (SDMs) are also widely used in many other
ecological applications (Guisan and Zimmermann, 2000; Drew
et al., 2011). For example, they can be used for testing biogeographical, ecological and evolutionary hypotheses (Leathwick,
1998; Anderson et al., 2002; Graham et al., 2004), for assessing
species invasion and proliferation (Beerling et al., 1995; Peterson,
2003), for modeling species assemblages from individual species
predictions (Leathwick et al., 1996; Guisan and Theurillat, 2000;
Ferrier et al., 2002) and for future potential distribution (forecasting) (Nielsen et al., 2008; Murphy et al., 2012a,b). They can also be
used for locating biogeographic regions (Murphy et al., 2012a,b)
and for improving the calculation of ecological distances between
patches in landscape meta-population dynamics and gene flow
models (Guisan and Thuiller, 2005; Cushman and Huettmann,
2010). SDMs are nowadays a standard for virtually all conservation
management projects; it is also used by IUCN (www.iucnredlist.
org) to map the species global distribution range. By now, ignoring
species model predictions and for species management must
basically present a professional oversight (Braun, 2005). As a ‘best
professional practice’ (Drew et al., 2011; Huettmann, 2012) they
should be done for any species of conservation concern. This is
even more so when most data and tools required for predictive
modeling are easily and cheaply available now (e.g. Ohse et al.,
2009; Huettmann et al., 2011) and rank among the very best
software tools on the (commercial) market.
Therefore, our work goes beyond classic SDMs because (a) we
use a commercial algorithm implementation that is among the best
known data mining methods, (b) we have compiled extensive datasets not used before, (c) we use open access data with metadata,
(d) we also make our models publicly available, and (e) we employ
many metrics to convince on the validity and predictive performance (statistical and expert review based on latest information).
All of this comes as a multivariate package and as a single powerful
workflow.
The red panda (Ailurus fulgens; Taxonomic Serial Number TSN:
621846) is still a little known Himalayan member of Carnivora
(Glatston, 2010) that has adapted to a herbivorous diet in a humid
environment (Yonzon, 1989; Pradhan et al., 2001; Choudhary,
2001, Wei et al., 1999a,b; Glatston, 2010). Some authors now consider it to be two subspecies: A.f. fulgens (in the western region
such as Nepal and adjacent Buthan and Sikkhim), and A.f. styani
mostly in China (Groves, 2011). Red panda is generally described
to inhabit multiple vegetation types, including evergreen, mixed
broad-leaf, deciduous and coniferous forests. The species is documented to live between an elevation range of 2000–4000 m above
sea level (asl), which is associated with dense bamboo thicket
understories (Yonzon, 1989; Wei et al., 1999a,b; Pradhan et al.,
2001; Choudhary, 2001; Glatston, 2010). It prefers steep northand eastward facing slopes due to the associated rainfall regime,
and because of its vegetation/food (Yonzon, 1989; Wei et al.,
151
1999a,b; Wei and Zhang, 2011). In the wild, red panda are difficult
to detect, track, observe and study though (Wei et al., 1999a,b).
This is due to their elusive nature, arboreal habit and the sole
occurrence in remote and inaccessible areas (Roberts and
Gittleman, 1984). Conflicts with humans are rampant (e.g. Fox
et al., 2002; Glatston and Gebaur, 2011), but no reliable population
estimates exist (Ziegler et al., 2010). The red panda is listed as ‘Vulnerable’ because its population is estimated <10,000 mature individuals with a continuing decline of greater than 10% over the
next 3 generations (estimated at 30 years). Geo-referenced survey
data on red panda distribution with a research design for such
cryptic and rare species are often sparse and clustered. It is here
where predictive modeling can help overcome sampling problems
and generate reliable, consistent and transparently derived estimates over wider areas (sensu Drew et al., 2011).
Since proven so successfully elsewhere (Guisan and
Zimmermann, 2000; Cushman and Huettmann, 2010; Drew et al.,
2011), a consistent model-based prediction of red panda distribution across the Hindu-Kush Himalaya (HKH) region would contribute in documenting its spatial distribution, habitat preferences and
as a baseline aiding management practices. These are all details
that are highly needed and beneficial for the red panda Action Plan
and the Recovery Team and PVAs for instance (Jnawali et al., 2012).
As a best practice role model, here we attempt to develop an
immediate and rapid assessment of quantitative and accurate
information on red panda distributions in eight nations of the
HKH region and for a science-based conservation management
for this globally threatened species. To bring the conservation community closer together, we aim that this work can be used and
assessed by others within the global community for efficient red
panda conservation. While this can only be one step for good progress, it should still serve as a powerful template for similar species, habitats and applications (see Ohse et al., 2009; Huettmann
et al., 2011). If successful, we think this can set a suitable standard
for other species management schemes to build on for progress
and for a more international scale even.
2. Materials and methods
2.1. Study area, field data collection and metadata
The Hindu-Kush Himalaya (HKH) region is a significant global
landscape (Ives, 2012). It consists of eight vast and diverse Asian
nations: Afghanistan, Pakistan, China, Nepal, India, Bhutan, Bangladesh and Myanmar; it comprises of 3,441,719 km2 (Table 1, Fig. 1).
The HKH region is tightly linked with most global economic and
ecological processes, e.g. for climate, as well as a resource provider
to India, China, and partly to Russia and the western world
(Winters and Yusuf, 2007; Huettmann, 2012). Glacier-fed river systems cover the HKH nations and include major rivers such as Ganges, Yangtsekiang, Yellow River and Brahmaputra. Several oceans
also rely for their freshwater inflow on the HKH region. In addition
to its complex ecological, human and religious set up (Huettmann,
2012), this vast region is infamous for its difficulty to survey. Rugged mountainous terrain covered by dense forest with often dense
bamboo undergrowth creates difficulties to observe red panda
directly in the field (Wei et al., 1999a,b; Pradhan et al., 2001;
Zhang et al., 2004). Therefore, feces (latrine sites) of red panda
can be considered as the most reliable indirect evidence of red
panda presence because animals usually defecate a midden of feces
at the feeding site (Wei et al., 1999a,b; Jnawali et al., 2012; Pradhan
et al., 2001). Tracks however are usually too soft to be found on the
ground. Consequently, most of our ‘presence only’ data come from
geo-referenced fecal midden (n = 1093), with a smaller proportion
consisting of direct sightings (n = 27) in Nepal (see data in the
152
K. Kandel et al. / Biological Conservation 181 (2015) 150–161
Table 1
The eight Hindu-Kush Himalaya (HKH) nations and their areas, the predicted red panda distribution within, and the outcome of the predictive Random Forest model. It is
noteworthy that our expert opinion and the available literature are all in general agreement with the predicted presence or pseudo-absence of red pandas in the respective
nations.
Nations
Total national
area in km2
Part of the nation falling in HKH as km2
(proportion of total country area)
Predicted red panda area in km2 (proportion of area in
HKH)
Based on expert cutoff
(% of total predicted)
Based on 95 percentile
threshold (% of total
predicted)
China
Nepal
India
Myanmar
Bhutan
Pakistan
Bangladesh
Afghanistan
9,596,960
147,181
3,287,263
677,000
38,394
796,096
147,570
652,000
1,647,725 (17%)
147,181 (100%)
482,920 (14%)
317,640 (47%)
38,394 (100%)
404,195 (51%)
13,189 (9%)
390,475 (60%)
8500 (26.1%)
17,400 (53.4%)
3200 (9.8%)
2900 (8.9%)
600 (1.8%)
0 (0%)
0 (0%)
0 (0%)
13,100 (27.8%)
22,400 (47.6%)
5700 (12.1%)
5000 (10.6%)
900 (1.9%)
0 (0%)
0 (0%)
0 (0%)
Total
15,342,464
3,441,719 (22%)
32,600 (100%)
47,100 (100%)
Predicted presence/absence from
Random Forest model outputs
Presence
Presence
Presence
Presence
Presence
Absence
Absence
Absence
Fig. 1. Map of study area, protected areas and best publicly available ‘presence only’ points for red panda in the Hindu Kush-Himalaya (HKH; from ICIMOD.org).
institutional repository https://scholarworks.alaska.edu/handle/
11122/1012 as well as metadata in https://scholarworks.alaska.
edu/handle/11122/1012 and published with NBII online http://
goo.gl/vUWJZ4).
The overall work flow for our predictive model and its field
work and digital data compilation is presented in Fig. 2. The
Department of National Parks and Wildlife Conservation of
Government of Nepal granted research permission to carry out surveys. Searching along elevational contour lines, as well as transect
surveys and surveys along small forest trails and opportunistic
sightings of species presence are the most common data collection
techniques in the mountain topography for the elusive and shy red
panda (Jnawali et al., 2012; Pradhan et al., 2001). While maintaining all relevant data details, here we pool sightings from all survey
techniques to obtain the best possible ‘presence only’ record dataset for the public record as a legacy. The geo-referencing of these
data was done with an eTREX 12 channel GPS (Global Positioning
System) and we recorded decimal degrees (5 decimals); the WGS
84 geographic datum was used. Most records include altitude
information in meters measured by an altimeter in the field. The
field data were collected between 2007 and 2011 from various
parts of Nepal by the authors (for more details see Metadata or
contact authors; see Fig. 1 for data point distributions).
2.2. Compilation of public and open access spatial (GIS and
environmental) data
We compiled bioclimatic data (derived from the monthly
temperature and precipitation values) (www.worldclim.org;
30 arcsecond pixels for bio1 to bio19 (Hijmans et al., 2005) and
masked them with the boundary of HKH defined by the International Centre for Integrated Mountain Development (ICIMOD) in
a Geographic Information System (ArcGIS 10, ESRI, 2011) freely
available to us as a campus license. Similarly, we downloaded a
Digital Elevation Model (DEM) with a 30 m resolution for HKH
from the USGS site (http://gdex.cr.usgs.gov/gdex/) and masked it
with the study area boundary. We fixed a small set of DEM errors
(e.g. a few coastline pixels) with a DEM created from 20 m to 40 m
contours (USGS, 2004). We used the corrected final DEM to prepare
K. Kandel et al. / Biological Conservation 181 (2015) 150–161
153
Fig. 2. The work flow for the predictive red panda model and its field work and digital data compilation and analysis.
an aspect map (circular degrees) and a slope map (degrees steepness) in ArcGIS 10.
2.3. Preparation and processing of spatial data
As a next step, we used the publicly available Geospatial Modeling Environment (GME) software (http://www.spatialecology.
com/gme/) to generate a total of 62,880 pseudo-absence (random)
points in the HKH region for habitat modeling using the
genrandompnts (Generate Random Points). For a representative
landscape sampling we used a 10 km distance rule to avoid any
clumping in the random points for representative background
sampling. Initially, we also used the pseudo-absences only for
Nepal as a test but this approach was dismissed because it did
not generalize well and for the entire HKH region. Next, a total of
79,597 lattice points (=a set of regular 10 km-spaced grid points)
were generated for the HKH study area using Generate Regular
Points in Polygons in order to create the ‘prediction to’ data. We
then used the GME tool isectpntrst to extract (=’drill down’) the
underlying point values for the different GIS layers. We extracted
underlying values for 1119 presence points, 62,880 random points,
and for 42,435 (predict to) lattice points from all predictor layers.
154
K. Kandel et al. / Biological Conservation 181 (2015) 150–161
2.4. Model building process, predictions and ecological distribution
niche assessment
We built our ensemble model using the individual algorithms of
Random Forest (RF), TreeNet (TN), Multiple Adaptive Regression
Splines (MARS) and Classification and Regression Trees (CART).
These algorithms are proven to be very powerful for many applications and they are widely used (Elith et al., 2006; Magness et al.,
2008; Huettmann et al., 2011), including in major commercial
and global security sectors. While several implementations exist
for such algorithms, e.g. in R, here we used the Salford Systems
Ltd version and which is known to perform extremely well (see
Herrick, 2013 for a comparison) and as a global market-leader for
those algorithms. We used this high-end software for data mining
in a highly relevant but complex international conservation context because (a) it offers a realistic and rapid approach to complex
and ‘messy data, (b) it is widely available and as a free trial version
(time-limited) to interested students and scholars, (c) it can easily
be linked with GIS (via tables in txt, ASCII format), (d) it is created
and highly endorsed by statistical and mathematical experts in the
field, (e) it is able to extract the major signals from data even when
data are skewed and imbalanced, and (f) it usually performs superior to the R implementations (Herrick, 2013) and when compared
with other versions and implementations. The software we used
creates robust models even when using a small and/or poor quality
number of ‘presence only’ data (Herrick, 2013, see also Hernandez
et al., 2006 for a general comparison elsewhere) using ‘balanced’
option. RF and TN can be considered an ensemble in itself (that’s
because they employ bagging and stochastic gradient boosting
employing hundreds of trees). It is well-established that in niche
modeling, and as far as the potential niche is concerned, incomplete and somewhat biased sampling (presence and random habitat) is usually not a problem for the inference and results (Kadmon
et al., 2004; Drew et al., 2011; Tessarolo et al., 2014). Also, using
Worldclim data in concert with elevation, slope and aspect is not
a problem then when such advanced machine learning software
is used (rather than MaxEnt and R implementations such as in BioMod http://r-forge.r-project.org/projects/biomod/). It can handle
such data, interactions, stopping rules, weighting and complexities
(Craig and Huettmann, 2008) (see http://www.salford-systems.com/ and (Drew et al., 2011) for details on bagging (out of
bag OOB sampling) and stochastic gradient boosting algorithms
we used). Our data are distributed over wide areas and thus generally lack high resolution autocorrelation. Further, the tree-based
algorithms we use and for the ensemble overall are rather robust
against autocorrelation also (Cushman and Huettmann, 2010;
Drew et al., 2011). Many approaches exist to obtain and create best
and most suitable ensemble models (Araujo and New, 2007; JonesFarrand et al., 2011). Here we created and tested two versions of
ensemble models from the individual models. The first one used
the average from indices obtained from each model (as done in
Hardy et al. (2011)). The average was derived within the ArcGIS
attribute table and its columns using ‘Field Calculator’ based on
all predicted individual relative occurrence indices (ROI) for the
algorithms used. For a test and comparison, we then created a second ensemble model, which forced all indices into 0 (pseudoabsence) and 1 (confirmed presence), and which was then also
averaged (for methods and formulae, see https://scholarworks.alaska.edu/handle/11122/2496). For area estimation of red
panda habitat in the HKH study area, we counted for each nation
the number of lattice points with an Ensemble ROI value above
the threshold (cut-off), and the number counted was multiplied
by 10 ⁄ 10 km2 because they were created with a 10 km-spaced
distance between lattice points.
We believe that this approach can help to obtain more meaningful results because (i) it is based on empirical data, (ii) it uses
a consistent algorithm, and (iii) it corrects through the standardization of the indices for contradicting averages (in machine learning, the resulting response variables are not automatically and
symmetrically distributed between 0 and 1 (Breiman, 2001a,b;
Cutler et al., 2007). Ensembles are a relatively new approach, and
with many unexplored options and settings still (Araujo and
New, 2007; Hardy et al., 2011). Here we started a first model
approach, but we make all data available for each model algorithm
to other users who may want to apply them for a wider assessment
and in other combinations and optimizations.
Finally, our models got assessed for validity and prediction
quality in five ways (similar done than Elith et al., 2006;
Huettmann et al., 2011): as a first metric we used the usual ROCArea under the Curve (AUC) metric, as commonly done (Pearce
and Ferrier, 2000). Using ROC and with unconfirmed absence (contaminated data) can though potentially result into a somewhat
flawed assessment (usually still underestimating the performance
because the pseudo-absences could include some presences and
blurs the presence–absence signal). Our spatial approach, and
where we include the entire HKH region with known national
absence locations (e.g. for HKH nations like Afghanistan and Pakistan) ‘to predict to’ should buffer against that. It performs like a
natural experiment in space for entire nations within the HKH
study area confronting our model with reality. It allows to assess
the predictions for areas where red panda is not known to occur
(our second performance metric). Third, we used some expert
opinions and known red panda distributions on the ground
(Drew and Perera, 2011). Fourth, we used a 95% confidence interval
of the predictions and compared it with the expert knowledge and
whether our maps remain valid for a trend. Last and fifth, we were
able to obtain brand new field data from co-workers and confronted them with our model also for a test.
3. Results
3.1. Model performance
Based on empirical data, here we present for the first time a
rapid assessment of an open access spatial predictive ensemble
model for a globally threatened species: the red panda. All
obtained data (https://scholarworks.alaska.edu/handle/11122/
2496), models (https://scholarworks.alaska.edu/handle/11122/
2496) and diagnostics (see ROC curves below and https://scholarworks.alaska.edu/handle/11122/2496) can be used for immediate
use in the HKH nations, or can be tested, assessed and fine-tuned
further. We were able to compile the best available red panda data
and describe the ecological niche for this species in the HKH region
in quantitative terms, and subsequently will be able to provide
assessment metrics. Except for MARS, all models within the
ensemble performed rather well on these data, e.g. a ROC over
95%. The tree-based models, Random Forests and CART, are among
the leading algorithms for red panda distribution predictions, followed by TreeNet; their single models did not differ much from
the overall ensemble model prediction. However, from our assessments of the highly performing ensemble, the Random Forest is the
best model prediction of the red panda, thus far (it should be
repeated here that some modelers refer to Random Forest as an
ensemble model in itself due to the bagging procedure it employs).
3.2. Variable importance for different models
From the four machine learning algorithms, the results for the
top predictors to describe model predictions (Tables 2 and 3) generally showed that temperature variables are more important than
precipitation. As a summarizing result of the Ensemble Model runs,
K. Kandel et al. / Biological Conservation 181 (2015) 150–161
3.3. Model validation and area estimates
Table 2
Summary and meaning of variables for the ensemble models.
Mean
importance
value
in ensemble
(percent)
Predictor
Biological meaning
How
often used
in top 5
BIO1
BIO2
Annual mean temperature
Mean diurnal range (mean of
monthly (max temp–min temp))
Temperature seasonality (standard
deviation ⁄ 100)
Max temperature of warmest
month
Min temperature of coldest month
Temperature annual range (BIO5BIO6)
Mean temperature of warmest
quarter
Mean temperature of coldest
quarter
Annual precipitation
Precipitation of wettest month
Precipitation seasonality
(coefficient of variation)
Precipitation of wettest quarter
Precipitation of driest quarter
3
3
24.9
25.1
4
48.4
2
35.6
3
4
20.9
100.0
3
28.0
2
16.7
2
2
2
14.8
26.9
5.5
1
2
24.4
2.8
BIO4
BIO5
BIO6
BIO7
BIO10
BIO11
BIO12
BIO13
BIO15
BIO16
BIO17
Table 3
Ensemble model results for ROC and for variable importance and by percent (for
explanations of variable see Table 2).
Variable
importance
rank
CART (% of
MARS (% of
importance) importance)
TreeNet (% of Random Forest (%
importance)
of importance)
1
2
3
4
5
ROC (variance
explained)
BIO7 (100)
BIO4 (99)
BIO2 (77)
BIO13 (66)
BIO18 (62)
0.99 (>98%)
BIO7 (100)
BIO5 (36)
BIO4 (35)
BIO10 (22)
BIO19 (9)
0.99 (>98%)
BIO7 (100)
BIO6 (67)
BIO1 (49)
BIO12 (46)
BIO13 (33)
0.99 (>98%)
155
BIO7 (100)
BIO5 (36)
BIO4 (35)
BIO10 (22)
BIO19 (9)
0.99 (>98%)
it stands out that only a few variables from the larger WorldClim
BIO set were favored. Generally, for best predictive performance
no strong single predictor was chosen but instead a multivariate
set of predictors was located. Predictors BIO4 and BIO7 were chosen four times and in tree-based models. Variables BIO1, BIO6 and
BIO10 were also more strongly selected. Other variables occurred
usually just once in the selected set. This is an interesting aspect
and outcome when applying ensemble models for complex conservation applications and inference.
While not all possible predictors such as soil and vegetation
were tested here, we used powerful proxy variables. All of these
details point toward the notion that a connected multivariate
set of climate predictors on a landscape-scale of HKH are among
the drivers of red panda distribution. This is not well-modeled
with singular, parsimonious linear algorithms, nor it is well
described for red pandas, yet (e.g. Glatston, 2010). Mostly, the
interactions are related to landscape-climate, and thus, somewhat
linked with altitude (Hof et al., 2012). Altitude was not selected
specifically, pointing to climate being a better and more detailed
predictor than altitude per se for red panda conservation. This
suggests that the micro-climate of the HKH region makes for a
powerful driver of the red panda ecological niche and for its distribution. The coarse link between climate, vegetation and altitude is well-known (Hof et al., 2012) and should still be
considered in any interpretation.
Prior to generalizing a model for a landscape it should be
assessed for its predictive performance (Drew et al., 2011). Thus,
model validation is essential for any predictive models (Pearce
and Ferrier, 2000; Huettmann and Gottschalk, 2011). If not provided, model results are subsequentially of lower inferential value,
just representing an untested hypothesis and a poorly substantiated claim (Huettmann and Gottschalk, 2011). While data mining
and predictive modeling can always be used for hypothesis creation, ideally, they should be used and assessed first for more applications and to provide real answers and a more robust inference
(Breiman, 2001a,b; Cushman and Huettmann, 2010; Drew et al.,
2011). As this is the first model of its kind, we lack a good and valid
high quality assessment data set for the study area yet. Such data
are either not available, not made public, or in most parts, were
never collected in a useful design and format. We believe that fixing this situation would make for great progress in future conservation research for this species and with red panda Action Plans
for instance (sensu Huettmann and Gottschalk, 2011; compare
with Jnawali et al., 2012).
In our models and based on ROC values, mathematically, Random Forest and CART are among the leading model predictions
(though they are relatively close to one another). Then we decided
to find the threshold value to define potential and not potential
areas in two different ways, by (1) expert analysis and by (2) statistical methods:
(1) Expert analysis: Maps were prepared with different cut-off
values (such as above 0.1, above 0.2 and so on till above
0.9) and then got shared with some red panda experts available to us and from the literature for their assessment (see
disclosure of these 8 experts in https://scholarworks.alaska.edu/handle/11122/2496). After assessing the
spatial output visually, it was found that the Random Forest
model performed ‘best’ (Fig. 3).
(2) Statistical analysis (95% confidence interval): For covering
the entire study area, we first developed an Inverse Distance
Weighting (IDW) raster surface for the predicted indices
from all the algorithms using the IDW tool in ArcGIS. Next,
the IDW surface value for the presence data set was
extracted in GME (see methods for details). Then a 95 percentile value (where 95% of presence points are within the
range) of the extracted values was determined and this value
was used as the threshold value for partitioning the predicted index into suitable and unsuitable climatic niche
(Fig. 3).
Table 1 presents how well the predictions match reality and
opinion of some experts. Table 1 also includes estimates and proportions of the red panda habitat in HKH, based on the ‘best’ model
(following the assessment of our experts). These findings were
derived from the 10 km scale pixel predictions. While initially this
might appear relatively coarse, they cover the entire HKH landscape and indicate the overall trend and for valleys, regions and
microclimates. Such findings are the first empirical quantitative
estimates for the vast HKH region, and provide us with a robust
quantitative baseline for distribution, population and future climate and impact modeling (our pixel size is also finer than most
IPCC models, for instance). We are also happy to report that the
area estimates resulting from a cut off value from experts’ opinion
vs the statistical 95% method are very close to each other and that
trends are almost equal (Table 4). We found that instead the cut-off
method per se, the actual algorithm is of bigger relevance for the
model prediction accuracy.
156
K. Kandel et al. / Biological Conservation 181 (2015) 150–161
red panda are found in lower mid-temperature ranges of HKH
and that it fluctuates in the medium range of the spectrum. Interpreting the response curves for these top predictors is meaningful
because they have been tested across ensemble model algorithms
and for the large HKH region based on thousands of pixels (which
basically each make for a localized test). This specific niche
description is the first of its kind for HKH. It shows in what peculiar
regions red panda occur in the landscape. This finding has wider
implications helping with topics like population estimates for a
species otherwise difficult to detect and to monitor in the field,
for its management, forecasting, prioritization and climate change
questions.
4. Discussion
Fig. 3. Frequency histogram of predicted vs reality red panda occurrence, showing
the 95 percentile threshold and the expert cut-off value (at the 87.13 percentile) for
the Random Forest model (a boosted density curve is also shown).
Matching up our predictions with the known status for this species in each of the HKH nations indicates a link with known red
panda hot- and cold spots. Further, this makes for a real-world test
(e.g. for Afghanistan and Pakistan where red panda is absent and
predicted by us to be absent). Thus, this large-scale model enables
for an expert-driven quality assessment.
3.4. Response curves (partial dependence plots) for identified main
predictors
Response curves can be very useful when based on stable models that are shown to generalize (=predict) well, and as it is the case
here. The Random Forest GUI implementation we used provided
for the best model, but it does not really provide partial dependence plots yet. Thus, we show the similarly-high performing
and tree-based TreeNet plots and to assess the top predictors that
were selected consistently. These figures read similar to linear
regression plots and resource selection functions (Ferrier et al.,
2002; Drew et al., 2011): it shows how the y-axis (response as a
relative index of occurrence) relates to changes along the x-axis
(a single predictor when all else is held constant and corrected
for). The response shape of the two top predictors, BIO4 and
BIO7 are presented in Fig. 5. Both response curves show the existence of thresholds, and they are located at 5000 units of the x-axis
for BIO4, and at 245 units for BIO7 (see methods and Table 2 for the
detailed description of predictors and their units). BIO4 indicates
that red panda can be found where temperature seasonality (standard deviation ⁄ 100) peaks between 4000 and 5000 units, and
with an intermediate occurrence of the species between 2000
and 4000 units. BIO7 shows us that the red panda niche is located
where Temperature Annual Range (BIO5-BIO6) lies between 18
and 24 units (degrees Celsius). In summary, this indicates that
4.1. New data and results
We present the first publicly available ‘presence only’ data for
the globally endangered red panda and the HKH region. These data
are documented with ISO metadata and available free of charge in
dSPACE (UAF) and GBIF.org via ICIMOD.org. Further, we used the
free and open access WorldClim and DEM data as powerful proxies
for red panda habitat preference modeling, which are also available
online, robust and widely used. While we lack soil and vegetation
predictors, we make the obtained ensemble model predictions also
freely available as GIS files and for a further public test and scrutiny.
Further, we assessed our model with five performance metrics and
showed that the obtained ‘best’ model is accurate, robust, and generalizing well for the HKH region. It allows for rapid assessments
and area estimations for instance (Ohse et al., 2009). Our results
and model trends remain very consistent, even when we confront
our model predictions with new data (a data set from SL with 35
sightings for the Rolpa region, and a data set with 11 sightings by
KK; see details in the https://scholarworks.alaska.edu/handle/
11122/2496). Noteworthy is the model prediction of known outlier
populations in the Meghalaya plateau, India (Glatston, 2010). The
consequent gain of our compiled and published data, the workflow
overall and the resulting model analysis and products provide for a
major progress regarding conservation management, transparency
and habitat assessments when it comes to red panda and for the
HKH region and its 8 nations overall.
4.2. Habitat area estimates for red panda
Choudhary (2001) estimated the global potential habitat of red
panda at about 142,000 km2, with China alone accounting for more
than half of the area. Similarly, Wei et al. (1999a,b) estimated
about 37,436 km2 as the potential red panda habitat within China.
Whereas based on our best Random Forest model prediction, and
for areas predicted above a determined 0.4 threshold level of ROI
(see Table 1), we estimated the current global potential habitat
of red panda as 47,000 km2 (just c. 33.17% of the area estimated
by Choudhary (2001)). Within that, China only holds 13,100 km2,
Table 4
Comparing the 95 percentile threshold and expert opinion cutoff value a for suitable and unsuitable area for red panda and their respective number
of included lattice points (10 km scale).
Algorithms
95 Percentile No. of lattice points %
threshold
within 95% threshold
Cut-off value No. of lattice points %
from expert from expert model
model
% Difference between 95
percentile threshold and expert
cut-off
CART
MARS
Random-forest
TreeNet
Ensemble
0.00000001
0.52523510
0.23562860
0.11955670
0.26993830
0.1
0.6
0.4
0.1
0.3
0
2.19
0.34
0.06
0.10
154
1726
471
299
358
0.36
4.06
1.10
0.70
0.84
154
798
326
325
314
0.36
1.88
0.76
0.76
0.73
K. Kandel et al. / Biological Conservation 181 (2015) 150–161
which is slightly above a quarter (27.8%) of the total suitable red
panda area (see Table 1 for details; Ziegler et al., 2010). Based on
best public empirical data and consistent prediction methods,
these estimates help to expose, assess and revise for 2014 with
scrutiny the previous assessment of the distribution of red panda
and of their published numbers; many of them were not really
based on transparent procedures and not using quantitative and
repeatable methods and best available public data. If red panda
has subspecies, then our findings would even be more worrisome
because we would just be dealing with population fragments
which are known to be even more problematic (Krebs, 2009;
Worboys et al., 2010).
We fully acknowledge that our species model was built on
Nepal data and of course with the wide HKH region somewhat
undersampled. However, that is precisely what machine learning
in a niche modeling context can deal with and overcome (see
Elith et al., 2006; Drew et al., 2011, see also Kadmon et al., 2004
for examples); it’s the strength of our highly cost-effective
approach. Despite the sampling limit, our five independent model
assessment metrics showed that our model performs reliably (see
Tables 3 and 4 and https://scholarworks.alaska.edu/handle/11122/
2496), confirming our approach with Breiman (2001a). Our model
building approach is realistic and provides such a good outcome
because Nepal features the complex set of the niche space for
model training. Next, we used high-end machine learning algorithms for such complex data. Also, the potential niche can easily
be predicted as long as similar data and habitat GIS layers exist
(and as it is the case here). It remains a peculiar fact that we have
a well-achieving model, but predict less red panda habitat overall,
as well as in China, than previously reported. We think that this
pattern is very real and provides now a study emphasize and for
species needs in its range, including for China’s habitats and
populations.
Similarly, Choudhary (2001) estimated 25,500 km2 (17.95%)
potential red panda habitat in India but we predicted only
3200 km2 (based on expert cut-off) and 5700 km2 based on the
95 percentile threshold. In the same way, Choudhary (2001) estimated 13,000 km2 (9.15%) as potential red panda habitat in Myanmar. However, our unique and advanced machine learning
prediction with GIS shows that Myanmar holds only 2900 km2
and 5000 km2 as a predicted red panda habitat by expert cut-off
and percentile threshold method respectively. So we think we have
a consistent pattern and model performance here, but which is not
in full agreement with published expert knowledge for area and
population estimates (Ziegler et al., 2010; Jnawali et al., 2012).
For red panda, no accepted population estimates really exist
(Ziegler et al., 2010). Such disagreements have been described
before for international species that are difficult to detect in the
field. This also occurs when assessing expert knowledge and which
was coined as somewhat subjective, data deficient and not repeatable by Drew et al. (2011, see also Huettmann and Gottschalk,
2011). Here we present a quantitative alternative based on best
available science and make it available for more assessments.
A first small-scale GIS-based overlay analysis (Yonzon et al.,
1991) estimated only 912 km2 of area suitable for the red panda
in Nepal which is only 0.61% of the total country area
(147,181 km2) whereas our Random Forest model has predicted
22,400 km2 (56.25% of total potential red panda area in HKH). This
is 15.20% of the total area of Nepal and makes Nepal a central focus
for this species and specifically for A.f. fulgens. We can provide good
support for our results and explanations. We think the reason for
this finding is because for Nepal (Yonzon et al., 1991) used only
three parameter area estimates: fir forest (Abies spectabilis), an elevation range of 3000–4000 m, and an annual precipitation
>2000 mm. However, data show that the red panda has clearly
been recorded beyond this elevation range, e.g., at elevations as
157
low as 2200 m in Ilam and Panchthar districts of eastern Nepal
(Kamal Kandel unpublished data) and at 2400 m in Singhalila
National Park in India (Pradhan et al., 2001; see also new field data
in https://scholarworks.alaska.edu/handle/11122/2496). Likewise,
the red panda has been recorded in other forest types beyond A.
spectabilis, for instance, Rhododendron forest and mixed broadleaf
forest in eastern Nepal and in Singhalila National Park (Pradhan
et al., 2001). Therefore, it is safe to assume that Yonzon et al.
(1991) somewhat underestimated the red panda distribution in
Nepal. Our predicted estimate there is c. 47% larger than Yonzon
et al. (1991). And this is explained because we have used a more
realistic and complete data set and large-scale approach for all of
Nepal to describe red panda habitat: 19 bioclimatic variables and
contemporary non-linear techniques and methods based on real
empirical newly compiled field data which were not used previously to predict the potential ecological niche of the red panda in
all of the HKH region. From our work here using consistent GIS pixels and methods, we believe that our estimates are more inclusive,
transparent and repeatable, and thus should be more sciencebased, consistent and realistic for all of the red panda habitat in
HKH, and as the model assessments convincingly show. We open
them up for a wider assessment by a global audience in this
publication.
We acknowledge that our models could predict the potential,
not the realized niche (Cushman and Huettmann, 2010), and thus
our model actually provides potentially some overestimates. But
if this would be true, the conservation status of red panda would
be even bleaker, making our model findings even more relevant
and urgent. On the other side, our models are done conservative,
consistent, and overall they predict well on the entire HKH landscape scale and match currently known distributions. This holds
we when evaluated it by our expert opinion, with ROCs from the
training data, and from what is known in the literature (we used
3 metrics) and also when confronted with new data (see https://
scholarworks.alaska.edu/handle/11122/2496 for 2 datasets by the
co-authors SL & KK). For instance, the new habitat patch for red
panda has been identified in Jajarkot, western Nepal (pers. comm.
Harihar Singh Rathour) which lies within our predicted range and
confirming our model further.
At minimum, our models provide publicly available numeric
and scientific estimates and trends for the distribution of red
panda obtained in a scientifically sound manner (e.g. use of bestavailable data and high-end algorithms that are available to use
freely). It helps to obtain a better foundation for a science-based
management of this species and for the entire HKH region and
its nations. This is achieved by providing information on where
the species is predicted to occur and survive. It also provides a
foundation for red panda reintroduction programs in the new areas
and for setting management priorities and for more pointed study
efforts. Our findings make for a common situation and when models are applied for the first time and for the conservation management of species (see Yen et al., 2004 for an example using
endangered species). Either way, our models help to form new
hypothesis, present new public science-based data, and invoke
more ground-truthing and fieldwork toward better red panda
models and for management in a readily available form for a global
public peer-review.
4.3. Climate warming and next conservation management steps
The map shown in Fig. 4 provides first support for wildlife managers in the region for preparing a site-specific Red Panda Conservation Action Plan (Jnawali et al., 2012 for a Population and Habitat
Viability Assessment PHVA), especially when accompanied later
with tools like GIS and Marxan (Ball et al., 2009; Huettmann,
2012) in the poorly known and highly threatened HKH region
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K. Kandel et al. / Biological Conservation 181 (2015) 150–161
Fig. 4. Random Forest prediction map (prediction assessed as ‘best’ from the ensemble).
Fig. 5. Shape of the two top predictors ((a) BIO7 and (b) BIO4; WorldClim dataset) to explain red panda occurrence (for details of the predictors please see Tables 1 and 2, and
text).
(Chettri et al., 2012). Based on such predictions (Magness et al.,
2010; Huettmann and Gottschalk, 2011; Lawler et al., 2011), they
can initiate to develop and assess further a biological corridor
and its gaps (Doko et al., 2008; Worboys et al., 2010; Huettmann,
2012) between remaining fragmented habitats and protected
areas; this can for instance help to keep habitats intact and to
reduce potential inbreeding depression of isolated red panda subspecies and their subpopulations (Wei et al., 1999a,b; Zhang et al.,
2006). Fig. 6 indicates already the urgent need to align existing
protected areas with the endangered red panda hotspots; currently, there is hardly a good match yet.
We find that GIS-based high-end open access ensemble models
can optimize model predictions by drawing from the benefits of
each model algorithm. When using more than over 20 landscape
predictors it becomes virtually untestable and for applying a traditional hypothesis in the classic concept of frequency statistics and
Bayesian predictions (Oppel et al., 2009; Breiman, 2001a; Elith
et al., 2006; Drew et al., 2011). This is a powerful and new
approach never applied to red panda before. However, it is a
known and applied technique elsewhere (see Araujo and New,
2007, for overview and Drew et al., 2011; Phillips and Dudik,
2008; Jones-Farrand et al., 2011; Hardy et al., 2011 for examples).
K. Kandel et al. / Biological Conservation 181 (2015) 150–161
159
Fig. 6. Locations of currently protected areas in the study area and red panda predictions: see mismatch of protected areas vs predicted occurrences (compare with Figs. 3 and
4 for best predicted red panda occurrence models. Here we present two resulting figures but which are very similar and for the general trend we report: (a) shows the
predictions based on a 95% percentile threshold; (b) shows the predictions based on an expert cut-off).
We propose more of those efforts made in regards to fine-tuning
these models and techniques, also triggering more data releases,
more thought, and a better policy. One of the current shortcomings
still is the lack of independent ground-truthing plots and data with
a research design, and to compare all available models for the
entire HKH region of over 8 nations.
Moving into truly open access data, with metadata and source
code published in appendices and institutional repositories as
new paradigms presents a ‘best practice’, major progress and how
wildlife and habitat gets managed (see Braun, 2005; Huettmann,
2005 for management, as well as Huettmann et al., 2011). This
is well promoted in the peer-reviewed literature and with
professional societies, e.g. Huettmann, 2005; Bluhm et al., 2010;
Zuckerberg et al., 2011) but still hardly realized and funded for conservation (Costello et al., 2014). Still, such data concepts and workflows provide progress and modern approaches in dealing with
natural resources in remote regions, and how they are sustainably
managed with adaptive methods and in a transparent fashion.
Climate warming is likely to drive accelerating shifts in species
distributions (Tse-ring et al., 2010; Carvalho et al., 2011; Araujo
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K. Kandel et al. / Biological Conservation 181 (2015) 150–161
et al., 2004), e.g. moving along altitudes. Thus, the climate-driven
habitat shifts would force the species to move out from protected
areas to unprotected areas and vice versa (Carvalho et al., 2011;
Forrest et al., 2012; Murphy et al., 2012a,b). Related impacts in
association with anthropogenic land use and resource use is also
known to affect the survival of red panda. Therefore, climate
change and its possible threats have to be integrated more into
efficient species conservation plans (Araujo et al., 2004; Forrest
et al., 2012) in space and time. This need is rather serious for species like red panda and which are already on the ‘edge’, on the
verge of extinction and which are sensitive to even a slight alteration in land use pattern in the Himalaya (Yonzon, 1991; Yonzon
and Hunter, 1991; Yonzon et al., 1991; Zuckerberg et al., 2011;
Huettmann, 2012). Noteworthy here is the rapid increase of the
human population in the vicinity of protected areas of the HKH
region (Ives, 2012), making for additional threats of many wildlife
and their habitat, including red panda.
Taking into account all of these reality facts for the HKH region
(Glatston, 2010; Ziegler et al., 2010; Ives, 2012; see Huettmann,
2012 for context and synthesis) and on the basis of these model
outputs, the next research goals should be to put these models to
a consistent test, ground-truth and institutionalize them with GIS
for consistent updates. The red panda distribution is to be investigated under future telecoupled climate-change scenarios and for a
large-scale spatial population viability assessment for this species
(Regmi et al., in prep, Jnawali et al., 2012; see Oppel and
Huettmann, 2010 for an example based on Random Forest). The
mountainous regions of HKH provide us here with a unique experimental test ground due to altitudinal gradients and the overall
multicomplexity for saving this charismatic but endangered species from irreversible gene loss and ultimate extinction.
Acknowledgements
ICIMOD, GMBA and their staff provided kindly facilitation and
workshops, and the EWHALE lab and students of University of
Alaska Fairbanks (UAF) provided technical support. The Department of National Parks and Wildlife Conservation of Government
of Nepal granted research permission to carry out surveys. We
thank Salford Systems Ltd for a free trial version of their data mining and machine learning software to the conservation research
community. We also thank the Geospatial Modeling Environment
(GME, H. Beier) for the free provision of software. We thank our
local field assistants for their help. H.S. Baral, H. Beyer, R. Hijmans,
G. Humphries, M. Lindgren, S. Linke, P. Regmi, M. Schmid, E. Spehn
and L. Strecker are kindly thanked for general support and answering queries, questions and administrative post and banking tasks.
Funding and in-kind support was gratefully received from Nagao
Natural Environment Foundation, Japan (to G.R.R.), Oregon Zoo
Foundation, U.S.A. (K.K.), The Rufford Small Grants Foundation,
U.K. (T.R.S, A.T. and H.P.S.), People’s Trust for Endangered Species,
U.K. (H.P.S.), Chester Zoo, U.K. (A.T.) and Idea Wild, U.S.A. (G.R.R.
and K.K). This is EWHALE lab publication # 125.
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